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Purpose – Model-based systems engineering (MBSE) is an important approach for the transforming process from “document-centered” to “model centered” systems engineering mode in equipment development, which can effectively shorten the equipment development cycle and improve product design quality. This paper aims to understand if MBSE enables to support manufacturing and equipment development. Design/methodology/approach – The paper opted a bibliometric analysis of MBSE in domain of advanced manufacturing from different perspectives such as publication volume, research team, sources and keyword co-occurrence. Findings – Firstly, the application of MBSE in advanced manufacturing can be roughly divided into three stages. And MBSE has been widely implemented globally and has gradually formed several noteworthy teams. Secondly, this article has identified some high-quality sources, with a large number of publications and citations, the most influential publications focus on the practice or guidance of digital twins and intelligent manufacturing. Thirdly, research can be divided into six categories, including systems engineering, digitalization, intelligent manufacturing, product design, model and architecture and MBSE applications. Research limitations/implications – Because of the chosen research approach, the visualized network tends to lose certain information such as a few keywords may be inaccurately categorized. Practical implications – This paper comprehensively study the research status of MBSE in advanced manufacturing and forecasts future research trends, emphasizing the combination of intelligent manufacturing and digitization. Originality/value – This paper fulfills an identified need to understand the current application status and future development trends of MBSE.

BackgroundAlzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions.MethodsIn this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN).ResultsOn this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models.ConclusionWe propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.

Building structural type information is the foundation for seismic risk assessment and management since it reflects the behavior of buildings under seismic load. However, in earthquake-prone regions, most of this information is out-of-date or nonexistent. This paper proposes a deep learning-based method for automatically identifying building structural types from unmanned aerial vehicle (UAV) oblique images. The method consists of four steps: (1) collect facades of buildings with different structural types by web crawler technology as a sample set; (2) construct a convolutional neural network with a facade prior knowledge attention branch and train the model using the sample set; (3) extract building facades from UAV oblique images based on the georeferencing results of feature points as the test set; (4) identify building structural types by inputting the test set into the trained model. Three cases have been selected to verify the feasibility and applicability of the method. The average recall rate of 85% and the average F1 score of 83% have been achieved in areas with regular building distribution. This method integrates multidisciplinary knowledge to provide a solution for rapid collection of building vulnerability information, and expands the role of oblique photography data in urban management and disaster prevention planning.

Objective To evaluate the effect of double filtration plasmapheresis (DFPP) on patients with hyperlipidemic acute pancreatitis (HL-AP). Methods The clinical data of the patients with hyperlipidemic severe acute pancreatitis (HL-SAP) or moderately severe acute pancreatitis (HL-MSAP) from January 2019 to May 2022 were collected. The patients were divided into two groups,the control group received conventional treatment, DFPP group received DFPP treatment on the basis of conventional treatment. Results Compared with the control group, the following parameters changed significantly in DFPP group, triglyceride (TG), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) decreased on day 1 and 2, estimated glomerular filtration rate (eGFR) increased on day 1, 2, 3 and 5, leukocyte and neutrophil counts decreased on day 1 and 2, hemoglobin (HGB) decreased on day 1, 2, and 3, C-reactive protein (CRP) decreased on day 1, 2, 3, 5, and 7. And the patients in DFPP group receiving mechanical ventilation decreased compared with that in control group. Conclusion Compared with conventional treatment, DFPP can reduce plasma TG faster, improve lung, liver and kidney function, reduce inflammatory markers, and facilitate early fluid recovery.

Introduction: Widely used in livestock breeding, residues of antibiotic drugs in milk have become a threat to food safety and human health. Current rapid detection technologies using colorimetric immunochromatographic strip tests (IST) lack the necessary sensitivity for on-site trace monitoring. Fluorescence-based detection in the near-infrared IIa’ (NIR-IIa’) region (1000 ∼ 1300 nm) has enormous potential due to greatly minimized auto-fluorescence and light scattering. Objectives: The aim of this work is to develop an ultrasensitive IST platform using NIR-IIa’ fluorescent nanoparticles as labels for multiplex antibiotic residues detection in milk. Methods: NIR-IIa’ fluorescent nanoparticles were assembled by encapsulating synthesized NIR-IIa’ fluorophores into carboxyl - modified polystyrene nanoparticles. The NIR-IIa’ nanoparticles were subsequently used as labels in an IST platform to detect sulfonamides, quinolones, and lincomycin simultaneously in milk. A portable fluorescent reader was fabricated to provide on-site detection. To further validate the developed IST platform, the detection was compared with LC-MS/MS in 22 real milk samples. Results: Fluorescent nanoparticles were synthesized with low energy emission (1030 nm) and large Stokes shift (>250 nm) showing a much higher signal-to-noise ratio compared with fluorophores emitting in the NIR-I region. The developed IST platform yielded a highly sensitive, simultaneous quantification of sulfonamides, quinolones, and lincomycin in milk with detection limits of 46.7, 27.6 and 51.4 pg/mL, respectively, achieving a wide detection range (up to 50 ng/mL). The IST platform showed good accuracy, reproducibility, and specificity with the portable fluorescent reader which could rapidly quantify in 10 s. These results were better than reported immunochromatographic assays using fluorescent labels, and remarkably, showed a higher recognition ability than LC-MS/MS for real samples. Conclusion: The utility of NIR-IIa’ fluorescence-based IST platform for the fast, sensitive, and accurate detection of antibiotics in milk was demonstrated, successfully verifying the potential of this platform in detecting trace materials in complex matrices.

BackgroundOlder patients with cancer experience cognitive impairment and a series of neurocognitive symptoms known as chemobrain due to chemotherapy. Moreover, older populations are disproportionately affected by chemobrain and heightened negative mental health outcomes after cytotoxic chemical drug therapy. Chinese acupuncture is an emerging therapeutic option for chemotherapy-induced cognitive impairment in older patients with cancer, despite limited supporting evidence. ObjectiveOur study aims to directly contribute to the existing knowledge of this novel Chinese medicine mode in older patients with cancer enrolled at the Department of Oncology/Chinese Medicine, Nanjing First Hospital, China, thereby establishing the basis for further research. MethodsThis study involves a 2-arm, prospective, randomized, assessor-blinded clinical trial in older patients with cancer experiencing chemobrain-related stress and treated with Chinese acupuncture from September 30, 2023, to December 31, 2025. We will enroll 168 older patients with cancer with clinically confirmed chemobrain. These participants will be recruited through screening by oncologists for Chinese acupuncture therapy and evaluation. Electroacupuncture will be performed by a registered practitioner of Chinese medicine. The electroacupuncture intervention will take about 30 minutes every session (2 sessions per week over 8 weeks). For the experimental group, the acupuncture points are mainly on the head, limbs, and abdomen, with a total of 6 pairs of electrically charged needles on the head, while for the control group, the acupuncture points are mainly on the head and limbs, with only 1 pair of electrically charged needles on the head. ResultsEligible participants will be randomized to the control group or the experimental group in 1:1 ratio. The primary outcome of this intervention will be the scores of the Montreal Cognitive Assessment. The secondary outcomes, that is, attentional function and working memory will be determined by the Digit Span Test scores. The quality of life of the patients and multiple functional assessments will also be evaluated. These outcomes will be measured at 2, 4, 6, and 8 weeks after the randomization. ConclusionsThis efficacy trial will explore whether Chinese electroacupuncture can prevent chemobrain, alleviate the related symptoms, and improve the quality of life of older patients with cancer who are undergoing or are just going to begin chemotherapy. The safety of this electroacupuncture intervention for such patients will also be evaluated. Data from this study will be used to promote electroacupuncture application in patients undergoing chemotherapy and support the design of further real-world studies. Trial RegistrationClinicalTrials.gov NCT05876988; https://clinicaltrials.gov/ct2/show/NCT05876988 International Registered Report Identifier (IRRID)DERR1-10.2196/53853

Frozen soil may cause structures to have different damage statuses, as revealed by earthquakes in northeastern China. ABAQUS (2019), a numerical simulation software application, was adopted to systematically and deeply study the structural seismic response, considering seasonal frozen soil–structure interaction under different ground motion intensities and soil ambient temperatures. The results showed firstly that the variation in soil ambient temperature had a great influence on the seismic response of the structure, as indicated by the damage status of the structure obtained through numerical simulation. Secondly, through further analysis of the numerical simulation results, the influence amplitude of different soil temperatures on the structural seismic response was quantitatively analyzed and systematically summarized. Finally, the structural seismic damage with negative ambient temperature could be significantly lower than that with positive temperature normally. Additionally, such an internal change mechanism was also objectively analyzed to verify the reliability of the conclusion.

The seismic stability analysis of a slope is a complex process influenced by earthquake action characteristics and soil mechanical properties. This paper presents a novel seismic slope stability analysis method using the relative residual displacement increment method in combination with the strength reduction method (SRM) and the actual deformation characteristics of the slope. By calculating the relative displacement of the key point inside the landslide mass and the reference point outside the landslide mass after each reduction, the safety factor of the slope is determined by the strength reduction factor (SRF) corresponding to the maximum absolute value of the relative residual displacement increment that appears after a continuous plastic penetration zone. The method eliminates interference caused by significant displacement fluctuations of key points under earthquake action and reduces the subjective error that can occur when manually identifying displacement mutation points. The proposed method is validated by dynamic calculations of homogeneous and layered soil slopes and compared with three other criteria: applicability, accuracy, and stability.

Abstract Generated by the immune system post-infection or through vaccination, the effectiveness of antibodies against emerging SARS-CoV-2 variants is crucial for protecting individuals from the COVID-19 pandemic. Herein, a platform for the multiplexed evaluation of SARS-CoV-2 neutralizing antibodies against various variants was designed on the basis of near-infrared (NIR) surface enhanced fluorescence by nano-plasmonic gold chip (pGOLD). Antibody level across variants (Wild-type, Alpha, Beta, Delta, Omicron) was confirmed by the sera from recovered-individuals who were unvaccinated and had infected with Wild-type, Delta, Omicron variants. However, the neutralizing activity against Omicron variant was markedly decreased for individuals infected by Wild-type (~ 5.6-fold) and Delta variant (~ 19.1-fold). To the opposite, neutralizing antibody from individuals recovered from Omicron variant infection showed weak binding strength against non-Omicron variants. Antibody evolution over time was studied with individuals 196–530 days post Wild-type infection. Decreasing IgG antibody titer accompanied by increasing IgG binding avidity with elongated post-infection period were observed for the sera from Wild-type recovered-individuals with different post-infection times, suggesting that after the primary infection, a great number of antibodies were generated and then gradually decreased, while the antibody matured over time. By comparing the IgG level of individuals vaccinated for 27–51 days with individual post-infection, we found that ca. 1 month after two doses of vaccination, the antibody level was comparable to that of 500 days post-infection, and vaccination could enhance IgG avidity more efficiently. This work demonstrated a platform for the multiplexed, high-throughput and rapid screening of acquired immunity against SARS-CoV-2 variants, providing a new approach for the analysis of vaccine effectiveness, immunity against emerging variants, and related serological study. Graphical Abstract

Mg–Zn–Y magnesium alloys comprising long-period stacking ordered (LPSO) phase have shown excellent strengthening effects, however, their deformation behavior and microstructure evolution under different strain rates and temperatures have not yet been thoroughly investigated. This work is based on the investigation of elevated temperature and high strain rate deformation behavior of an as-extruded Mg–2.5Zn–4Y magnesium alloy comprising LPSO phase, with the strain rates lying in the 700 s−1 to 2100 s−1 range, and temperature ranging from 293 K to 623 K. Electron backscattering diffraction (EBSD), scanning electron microscopy (SEM), and optical microscopy (OM) was employed for investigating the deformation microstructure. The results show that at early deformation the extension twinning coordinates the plastic deformation first, subsequently, pyramidal < c+a > slip and kinking of LPSO phase can also coordinate deformation for the experimental alloy at high strain rates at high temperatures. A peculiar phenomenon of misaligned LPSO phase structures (1600 s−1, 623 K) was also discovered. The fracture mode of the experimental alloy is mainly the cleavage fracture or the mixed ductile-brittle fracture during high-speed impact. A modified Johnson–Cook (J-C) constitutive model of the as-extruded Mg–2.5Zn–4Y was found to appropriately analyze the stress–strain values by comparing the computed results with the experimentally obtained data. It was, thus, demonstrated that the modified J-C model can make a reasonable prediction about the dynamic mechanical behavior of a Mg–2.5Zn–4Y magnesium alloy in its as-extruded condition throughout a range of high temperatures and strain rates with a high degree of accuracy.

Abstract Background and objectives Up till now, there are still controversies about the specific indication of endoscopic resection for small gastric subepithelial tumors (gSETs) originating from muscularis propria. We aimed to investigate the safety of endoscopic resection and postoperative pathology analysis. Method The patients with primary small gSETs originating from muscularis propria, treated by endoscopic resection in the endoscopic center of Shengjing Hospital between January, 2011 and September, 2019 were enrolled. The complete resection rate, adverse events and clinicopathological features were recorded. Result A total of 936 patients with 972 gastric SETs ≤ 2 cm originating from muscularis propria were included in our study. All the lesions were successfully treated by endoscopic resection. Nearly half of lesions were proved to be gastrointestinal stromal tumor (GIST) [n = 411 (42.3%)] according to postoperative pathology. All the objects were further subdivided into 2 groups, ≤ 1 cm, > 1 and ≤ 2 cm gSETs. The risk of gastric GIST of intermediate/high risk in the group (> 1 and ≤ 2 cm gSETs) is 8.41 times as that of gastric GIST in the group (the size of gastric ≤ 1 cm gSETs) (P

The article discusses the Neodeterministic (NDSHA) approach to seismic hazard assessment, which aims to prevent seismic disasters rather than simply addressing their consequences. The NDSHA method takes into account the tensor nature of earthquake ground motions and has been proven reliable through experiments conducted over the past 25 years. The article also highlights the organization of the book "Earthquakes and Sustainable Infrastructure- Neodeterministic (NDSHA) Approach Guarantees Prevention Rather Than Cure," which compiles developments in the NDSHA approach and its application in various countries and regions. The book covers topics such as hazard analysis, earthquake forecasting, site response evaluation, and the seismic performance of infrastructure. This book provides a comprehensive exploration of seismic hazard assessment, focusing on the gap between engineering principles and the needs of seismologists, engineering seismologists, and policymakers. It emphasizes the importance of designing infrastructure to withstand future earthquakes and prevent damage. [Extracted from the article]

Coal gangue sorting is a necessary process in coal mine production, and removing gangue is the basis for the coal production of clean energy; it is also an important approach to reduce the cost of washing, improve the grade of finished coal and increase the economic efficiency of coal mining enterprises. For the problem of high similarity and low-degree dynamic recognition of coal and gangue, a coal gangue target detection method based on improved YOLOv5s is proposed. Based on the YOLOv5s network, the decoupled head and SimAM attention mechanism are introduced and the CSP module in the neck part of YOLOv5s is replaced with the VoV-GSCSP structure. The experimental results show that the proposed method improves the mAP value by 6.1% over YOLOv5s in the gangue target detection task, while maintaining a higher detection speed. The coal gangue classification precision reaches 99.7% when tested on 1479 images. Compared with YOLOv5 series, YOLOv7 series, SSD and Faster-RCNN, the proposed method invariably yields higher precision and detection speed to meet the requirements of real-time detection. The experiments prove that the method proposed in this paper can be applied to the coal gangue sorting industry for fast and high-precision identification of coal gangue.

Although online health communities are popular in supporting mental health, factors leading to the helpfulness of mental health information are still under-investigated. Based on the elaboration likelihood model and motivation theory, we incorporate two types of health information-related constructs, i.e., information quality (central route) and responders’ effort (peripheral route), and adopt reputation as an extrinsic motivation to build our model. We crawl data from a Chinese online mental health community and extract 11 key variables, and then analyze the model with negative binomial regression. The empirical results indicate that the effect of the length of health information on its helpfulness votes is positively significant, while the effect of readability of health information on its helpfulness votes is relatively negative. In terms of responders’ effort, both the timelines of the response and interactive feedback have a significant positive impact on helpfulness of health information votes, while these effects are negatively moderated by the online reputation of responders. This study contributes to the literature on information evaluation mechanisms in online health communities.

Differential equations are useful mathematical tools for solving complex problems. Differential equations include ordinary and partial differential equations. Nonlinear equations can express the nonlinear relationship between dependent and independent variables. The nonlinear second-order neutral differential equations studied in this paper are a class of quadratic differentiable equations that include delay terms. According to the t-value interval in the differential equation function, a basis is needed for selecting the initial values of the differential equations. The initial value of the differential equation is calculated with the initial value calculation formula, and the existence of the solution of the nonlinear second-order neutral differential equation is determined using the condensation mapping fixed-point theorem. Thus, the oscillation analysis of nonlinear differential equations is realized. The experimental results indicate that the nonlinear neutral differential equation can analyze the oscillation behavior of the circuit in the Colpitts oscillator by constructing a solution equation for the oscillation frequency and optimizing the circuit design. It provides a more accurate control for practical applications.